Wenji Li, Lihong Xie, C. Sivaparthipan, C. Vignesh
{"title":"人工智能和机器人在手术极端环境下","authors":"Wenji Li, Lihong Xie, C. Sivaparthipan, C. Vignesh","doi":"10.3233/JIFS-219011","DOIUrl":null,"url":null,"abstract":"Robotic surgery offers surgeons a greater degree of accuracy, versatility, and control than with standard techniques for other kinds of complicated procedures. The robotic surgery technology offers numerous advantages for patients and leads to unforeseen effects that are easier to predict when such a complex interactive device is used for treatment. The challenging complications that are occurred during robotic surgery include, risk of human error while operating the robotic system and the possibility for mechanical failure. The paper proposes Robot Assisted - Remote Center Surgical System (RA-RCSS) to improve mechanical malfunction threat and practical skills of surgeons through intra practice feedback and demonstration from human experts. A mask region-based supervised learning model is trained to conduct semantic segmentation of surgical instruments and targets to improve surgical coordinates further and to facilitate self-oriented practice. Furthermore, the master-slave bilateral technique is integrated with RA-RCSS to analyze the mechanical failures and malfunctions of the robotic system. The emerging safety standard environment is presented as a key enabling factor in the commercialization of autonomous surgical robots. The simulation analysis is performed based on accuracy, security, performance, and cost factor proves the reliability of the proposed framework.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI with robotics in surgery extreme environments\",\"authors\":\"Wenji Li, Lihong Xie, C. Sivaparthipan, C. Vignesh\",\"doi\":\"10.3233/JIFS-219011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Robotic surgery offers surgeons a greater degree of accuracy, versatility, and control than with standard techniques for other kinds of complicated procedures. The robotic surgery technology offers numerous advantages for patients and leads to unforeseen effects that are easier to predict when such a complex interactive device is used for treatment. The challenging complications that are occurred during robotic surgery include, risk of human error while operating the robotic system and the possibility for mechanical failure. The paper proposes Robot Assisted - Remote Center Surgical System (RA-RCSS) to improve mechanical malfunction threat and practical skills of surgeons through intra practice feedback and demonstration from human experts. A mask region-based supervised learning model is trained to conduct semantic segmentation of surgical instruments and targets to improve surgical coordinates further and to facilitate self-oriented practice. Furthermore, the master-slave bilateral technique is integrated with RA-RCSS to analyze the mechanical failures and malfunctions of the robotic system. The emerging safety standard environment is presented as a key enabling factor in the commercialization of autonomous surgical robots. The simulation analysis is performed based on accuracy, security, performance, and cost factor proves the reliability of the proposed framework.\",\"PeriodicalId\":44705,\"journal\":{\"name\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/JIFS-219011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fuzzy Logic and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JIFS-219011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Robotic surgery offers surgeons a greater degree of accuracy, versatility, and control than with standard techniques for other kinds of complicated procedures. The robotic surgery technology offers numerous advantages for patients and leads to unforeseen effects that are easier to predict when such a complex interactive device is used for treatment. The challenging complications that are occurred during robotic surgery include, risk of human error while operating the robotic system and the possibility for mechanical failure. The paper proposes Robot Assisted - Remote Center Surgical System (RA-RCSS) to improve mechanical malfunction threat and practical skills of surgeons through intra practice feedback and demonstration from human experts. A mask region-based supervised learning model is trained to conduct semantic segmentation of surgical instruments and targets to improve surgical coordinates further and to facilitate self-oriented practice. Furthermore, the master-slave bilateral technique is integrated with RA-RCSS to analyze the mechanical failures and malfunctions of the robotic system. The emerging safety standard environment is presented as a key enabling factor in the commercialization of autonomous surgical robots. The simulation analysis is performed based on accuracy, security, performance, and cost factor proves the reliability of the proposed framework.
期刊介绍:
The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.